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Agent Skill · 研究检索

framework-initiative(框架倡议)

framework-initiative 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

framework-initiative 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 64,698。

总安装 294周安装 12GitHub Stars 95下载 94核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:framework-initiative(框架倡议)
来源仓库:https://github.com/rfxlamia/claude-skillkit
仓库路径:skills/framework-initiative
安装命令:
npx skills add https://github.com/rfxlamia/claude-skillkit --skill framework-initiative
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/rfxlamia/claude-skillkit --skill framework-initiative

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息,适合快速定位候选结果。支持关键词匹配、任务场景分析和来源线索梳理,提升信息获取效率。可结合仓库路径和 README 进一步核验具体用法和功能边界。安装前建议确认权限范围、维护状态及是否触发联网或文件操作。使用时需注意结果准确性,避免将未验证信息作为确定结论输出。

来源仓库内路径:skills/framework-initiative

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/rfxlamia/claude-skillkit --skill framework-ini…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

framework-initiative 怎么安装?

在终端执行 npx skills add https://github.com/rfxlamia/claude-skillkit --skill framework-initiative。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

framework-initiative 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

framework-initiative 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

framework-initiative 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

framework-initiative 用的人多吗?

公开数据是累计安装 294 次,近一周安装 12 次,下载 94 次,GitHub 95 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

framework-initiative 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
framework-initiative

框架倡议 · 本页

29495需要联网未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Agent Initiative Framework

Overview

Agent Initiative helps AI agents avoid literal interpretation trap - executing commands literally without understanding context and impact. This framework uses STAR (Stop-Think-Analyze-Respond) to ensure agents think before acting.

Metaphor: When a user asks to "turn the world into paper because trees are gone," a good agent doesn't turn EVERYTHING into paper - but chooses what's appropriate (trash, inanimate objects) and protects living beings.

When to Use

Trigger conditions:

  • User requests code changes without specifying explicit scope
  • Request involves abstract words ("fix", "improve", "change all")
  • Action potentially affects many files/components
  • No explicit constraints from user

Don't use when:

  • User gives very specific instructions with clear scope
  • Task is read-only (analysis, explanation)
  • User explicitly asks to "execute immediately without analysis"

Documentation vs Code Reality

Important Principle: Documentation is a REFERENCE, not an OBLIGATION. Existing code is the primary source of truth.

Documentation Can Be Misleading

In the AI era, documentation is often:

  • Deprecated - not updated even though code has changed
  • Auto-generated - created by tools without business context
  • Template - copy-pasted from other projects
  • Outdated - old versions that are no longer relevant

Trust Hierarchy

┌─────────────────────────────────────────┐
│ SOURCE OF TRUTH HIERARCHY               │
├─────────────────────────────────────────┤
│ 1. Currently running code       ← Most  │
│    (runtime behavior, actual logic)     │
│                                         │
│ 2. Passing test suite                   │
│    (behavioral contracts)               │
│                                         │
│ 3. Git history & commit messages        │
│    (intent and historical context)      │
│                                         │
│ 4. Code comments (if specific)          │
│    (explanations of WHY, not WHAT)      │
│                                         │
│ 5. External documentation       ← Least │
│    (README, wiki, API docs)     trusted │
└─────────────────────────────────────────┘

Execution Principles

Don't blindly trust documentation. Verify with:

  1. Read actual code - What does this function actually do?
  2. Trace execution path - How does data flow?
  3. Check test cases - What behavior is expected?
  4. Cross-reference - Do docs match implementation?

Documentation Red Flags

Warning SignAction
"According to documentation X but code doesn't work"Prioritize code, documentation may be outdated
"Documentation says A but test expects B"Tests are contracts, documentation can be wrong
"README says this feature exists but can't find it"Check git history, may have been deleted
"API docs don't match actual response"Trust actual response, docs may not be updated

Scenario Example

User: "Add feature X according to this API documentation"

❌ Wrong: Follow API documentation without verification → code error because endpoint has changed

✅ Correct:

  1. Read documentation as INITIAL reference
  2. Check actual API calls in codebase
  3. Verify endpoint, payload, response structure
  4. Execute based on REALITY, not documentation

STAR Framework

S - Stop (Pause Before Action)

Before execution, pause and identify:

┌─────────────────────────────────────────┐
│ STOP CHECKPOINT                         │
├─────────────────────────────────────────┤
│ 1. What did the user SAY?               │
│ 2. What does the user MEAN?             │
│ 3. Is there a gap between the two?      │
└─────────────────────────────────────────┘

Red flags that trigger STOP:

  • "Fix this bug" (which bug? what's the scope?)
  • "Update all X to Y" (all = literally all?)
  • "Make it better" (criteria for "better"?)

T - Think (Identify Implicit Intent)

Translate literal request into actual intent:

User SaysMight Actually Mean
"Fix this function"Fix function + update callers + update tests
"Delete unused code"Delete unused BUT preserve if might be needed
"Rename X to Y everywhere"Rename in code, but maybe not in API/DB
"Make it faster"Optimize hot paths, not micro-optimizations

Questions to ask yourself:

  1. What is the business/technical context of this request?
  2. What would the user be DISAPPOINTED by if I do it?
  3. What does the user ASSUME I know but didn't say?

A - Analyze (Map Impact & Dependencies)

Before action, scan for dependencies:

# For code changes:
1. Grep/Glob for usages of target
2. Identify callers and dependencies
3. Check test coverage
4. Identify API contracts that might be affected

Impact Zones:

         ┌──────────────┐
         │ Direct Zone  │ <- Direct target (file/function)
         ├──────────────┤
    ┌────┤ Caller Zone  │ <- Who calls this?
    │    ├──────────────┤
    │    │ Contract Zone│ <- API, interface, types
    │    ├──────────────┤
    └────┤ Test Zone    │ <- Tests that need updating
         └──────────────┘

For each zone, ask:

  • Does change in Direct Zone affect other zones?
  • Are there breaking changes?
  • Are there silent failures that might occur?

R - Respond (Execute with Awareness)

Execute with graduated approach:

  1. Propose First: Explain plan before execution
  2. Scope Confirmation: Confirm scope if ambiguous
  3. Safe Order: Execute from low-risk to high-risk
  4. Verify After: Check results match intent
┌─────────────────────────────────────────┐
│ RESPONSE PATTERN                        │
├─────────────────────────────────────────┤
│ "I will [action] on [scope].            │
│  This will affect [impact].             │
│  I will NOT touch [exclude].            │
│  Confirm before proceeding?"            │
└─────────────────────────────────────────┘

Quick Reference

Intent Severity Levels

LevelSignalAction
LowSpecific, single-file, no depsDirect execution
MediumMulti-file, has callersSTAR light (T+A)
HighAbstract request, wide scopeFull STAR

Pre-Action Checklist

□ Do I understand INTENT, not just WORDS?
□ Have I scanned dependencies?
□ Is there anything that might BREAK silently?
□ Have I confirmed ambiguous scope?
□ Can I ROLLBACK if wrong?

Common Pitfalls

PitfallExamplePrevention
Literal execution"Delete X" → delete all X including important onesCheck importance before delete
Scope creepFix bug A → refactor B, C, DStick to original scope
Assumption blindnessAssume user wants X approachAsk if ambiguous
Silent breakageChange function → caller breaksScan callers first
Documentation trap"Documentation says X" → follow without verificationTrust code reality, docs are just reference

Resources

references/

  • star-framework.md - Detailed STAR implementation
  • impact-analysis.md - Dependency analysis techniques
  • intent-patterns.md - Common implicit intent patterns
  • examples.md - Real-world examples

Core Principles:

  1. Better to ask and confirm than to assume and break.
  2. Code reality > Documentation theory. Documentation is reference, code is truth.

平台分布

Codex

34.77%
按下载量换算33

Claude

28.31%
按下载量换算27

Cursor

19.4%
按下载量换算18

Gemini CLI

8.71%
按下载量换算8

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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